A Clustering Algorithm for Classification of Network Traffic using Semi Supervised Data

Anjali Wankhede; Kailash Patidar

CALL FOR PAPERS : Feb-2026

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25-Feb-26

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A Clustering Algorithm for Classification of Network Traffic using Semi Supervised Data

Author(s):

Anjali Wankhede , SATYA SAI INSTITUTE OF SCIENCE & TECHNOLOGY, SEHORE; Kailash Patidar, SATYA SAI INSTITUTE OF SCIENCE & TECHNOLOGY, SEHORE

Keywords:

supervised data, labeled data, clustering, K-means, network traffic, classification

Abstract:

In traditional text classification, a classifier is built using supervised training documents of every class. This paper studies a different problem. Given a set P of documents of a particular class (called positive class) and a set U of unsupervised documents that contains documents from class P and also other types of documents (called negative class), we want to build a classifier to classify the documents in U into documents from P and documents not from P. The key feature of this problem is that there is no supervised negative document, which makes traditional text classification techniques inappropriate. So In this paper, we propose an effective technique to solve the problem. It combines the Rocchio method and the K-means technique for classifier network data. Experimental results show that the new method outperforms existing methods significantly.